I: Training with single training and testing partitions
1: Training approaches
library(TSstudio)
## Warning: package 'TSstudio' was built under R version 4.3.3
data(USgas)
ts_info(USgas)
## The USgas series is a ts object with 1 variable and 238 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2019 10
train <- window(USgas,
start = time(USgas)[1],
end = time(USgas)[length(USgas) - 12])
test <- window(USgas,
start = time(USgas)[length(USgas) - 12 + 1],
end = time(USgas)[length(USgas)])
ts_info(train)
## The train series is a ts object with 1 variable and 226 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2018 10
ts_info(test)
## The test series is a ts object with 1 variable and 12 observations
## Frequency: 12
## Start time: 2018 11
## End time: 2019 10
2: Forecasting with backtesting
USgas_partitions <- ts_split(USgas, sample.out = 12)
train <- USgas_partitions$train
test <- USgas_partitions$test
ts_info(train)
## The train series is a ts object with 1 variable and 226 observations
## Frequency: 12
## Start time: 2000 1
## End time: 2018 10
ts_info(test)
## The test series is a ts object with 1 variable and 12 observations
## Frequency: 12
## Start time: 2018 11
## End time: 2019 10
II: Forecasting evaluation
1: Residual analysis
library(forecast)
## Warning: package 'forecast' was built under R version 4.3.3
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
md <- auto.arima(train)
checkresiduals(md)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(2,1,1)(2,1,1)[12]
## Q* = 24.95, df = 18, p-value = 0.1263
##
## Model df: 6. Total lags used: 24
2: Scoring the forecast
fc <- forecast(md, h = 12)
accuracy(fc, test)
## ME RMSE MAE MPE MAPE MASE
## Training set 5.844136 97.81626 73.42657 0.1170672 3.522348 0.6376860
## Test set 37.847885 103.22848 81.46603 1.3107987 3.261643 0.7075062
## ACF1 Theil's U
## Training set -0.004183172 NA
## Test set -0.046708926 0.3404092
3: Forecast bencharmk
test_forecast(actual = USgas,
forecast.obj = fc,
test = test)
4
naive_model <- naive(train, h = 12)
test_forecast(actual = USgas,
forecast.obj = naive_model,
test = test)
accuracy(naive_model, test)
## ME RMSE MAE MPE MAPE MASE
## Training set -1.028444 285.6607 228.5084 -0.9218463 10.97123 1.984522
## Test set 301.891667 499.6914 379.1417 9.6798015 13.28187 3.292723
## ACF1 Theil's U
## Training set 0.3761105 NA
## Test set 0.7002486 1.499679
5
snaive_model <- snaive(train, h = 12)
test_forecast(actual = USgas,
forecast.obj = snaive_model,
test = test)
accuracy(snaive_model, test)
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 33.99953 148.7049 115.1453 1.379869 5.494048 1.000000 0.4859501
## Test set 96.45000 164.6967 135.8833 3.612060 5.220458 1.180103 -0.2120929
## Theil's U
## Training set NA
## Test set 0.4289964
III: Finalizing the forecast
1: Handling forcast uncertainty
md_final <- auto.arima(USgas)
fc_final <- forecast(md_final, h = 12)
plot_forecast(fc_final,
title = "The US Natural Gas Consumption Forecast",
Xtitle = "Year",
Ytitle = "Billion Cubic Feet")
2: Simulation
fc_final2 <- forecast(md_final,
h = 60,
level = c(80, 90))
plot_forecast(fc_final2,
title = "The US Natural Gas Consumption Forecast",
Xtitle = "Year",
Ytitle = "Billion Cubic Feet")
3: Horse race approach
fc_final3 <- forecast_sim(model = md_final,
h = 60,
n = 500)
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.3.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
fc_final3$plot %>%
layout(title = "US Natural Gas Consumption - Forecasting Simulation",
yaxis = list(title = "Billion Cubic Feet"),
xaxis = list(title = "Year"))
4
# set.seed(1234)
#
# library(TSstudio)
#
# # Sample data (replace with your actual time series data)
# data(USgas)
# Backtesting with train_model (corrected arguments)
#USgas_forecast <- train_model(USgas,
# method = "ETS", # Specify the forecasting method (e.g., ETS)
# h = 60, # Specify the forecast horizon (number of steps)
# error = "MAPE") # Specify the error metric (e.g., MAPE)
#
# # Access forecast results
# forecast <- USgas_forecast$forecast
#
# # Print summary statistics (optional)
# summary(forecast) # View basic descriptive statistics
#
# # Plotting options (see previous response for details)
#
#
# # Plotting options:
#
# # Option 1: Using autoplot (recommended)
# library(ggplot2) # Load ggplot2 for advanced plotting
#
# autoplot(forecast)